Developing objects and verifying them to be optimum has proven to be a difficult and arduous task for a number of classes of problems. Genetic algorithm methodologies have been developed to facilitate development of optimal solutions. In genetic algorithm methodologies, objects are represented by "genomes," which, in a series of iterations, are evaluated, selected in accordance with predetermined selection or fitness criteria, and mated and mutated in a selected manner. The result at the end of each iteration represents a set of objects which, in turn, are evaluated during a subsequent iteration. In each iteration, characteristics of those genomes that are selected in the selection step survive to the next iteration, in a manner similar to survival of genetic traits from one generation to the next under Darwin's theory of natural selection. Genetic algorithm methodologies thus facilitate construction of what might be described as "spaces" of objects which are searched for optimal solutions.